linkedin-post-generator

casper-studios/casper-marketplace · updated Apr 8, 2026

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$npx skills add https://github.com/casper-studios/casper-marketplace --skill linkedin-post-generator
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summary

Generate LinkedIn posts from shared source material, written in each user's personal style.

skill.md

LinkedIn Post Generator

Generate LinkedIn posts from shared source material, written in each user's personal style.

How It Works

  1. Personal style profile — stored locally at ~/.config/casper/linkedin-style.md (never committed)
  2. Source config — stored locally at ~/.config/casper/linkedin-sources.md (never committed)
  3. Shared source material — meeting transcripts, Slack dumps, docs in source-material/
  4. Prompt template — extraction rules, voice guidelines, few-shot examples in references/prompt-template.md

First Run: Style Setup

Check if ~/.config/casper/linkedin-style.md exists.

If it does NOT exist, run the style setup flow:

  1. Say: "Welcome to the LinkedIn Post Generator! Before we start, I need to understand your writing style."
  2. Say: "Share 3 LinkedIn posts that match the style you want. You can either paste the post links (e.g. https://linkedin.com/posts/...) or paste the text directly."
  3. Wait for the user to provide 3 posts
  4. If the user provides LinkedIn URLs, fetch the post content using the apify-scrapers skill:
    python ${CLAUDE_PLUGIN_ROOT}/skills/apify-scrapers/scripts/scrape_linkedin_posts.py search "{url}" --max-posts 1
    
    Extract the post text from the JSON output. If a URL fails to fetch, ask the user to paste that post's text instead.
  5. Analyze the posts for: tone, sentence length, vocabulary, formatting habits, hook style, CTA style, use of questions, paragraph length, overall energy
  6. Create ~/.config/casper/ directory if it doesn't exist
  7. Save the analysis to ~/.config/casper/linkedin-style.md using this format:
# LinkedIn Style Profile
Generated: [date]

## Tone
[analysis]

## Structure Patterns
[paragraph length, line breaks, formatting habits]

## Hook Style
[how they open posts]

## CTA / Closing Style
[how they end posts — questions, challenges, etc.]

## Vocabulary & Phrases
[distinctive phrases, word choices, energy level]

## Sample Posts
[the 3 original posts, for reference]
  1. Confirm: "Got it! Your style profile is saved. You can update it anytime with /casper:generate-linkedin-post --setup"

First Run: Source Material Check

After style setup completes (or if style exists but source-material/ is empty), check for source material:

  1. Check if source-material/ contains any .md files besides README.md
  2. If empty, guide the user:
    • Say: "You don't have any source material yet. I need content to generate posts from — meeting transcripts, notes, Slack conversations, etc."
    • Present options:
      • "Connect integrations" — run the --setup-sources flow to configure Fireflies, Slack, or Google Drive auto-pulling
      • "Paste something manually" — run the --add-source flow to let the user paste a transcript, notes, or other content
    • Wait for user choice and proceed with the selected flow
  3. If source material exists, proceed with generation

Normal Run: Post Generation

If style config exists and source material is available, proceed with generation:

  1. Read ~/.config/casper/linkedin-style.md
  2. Read ALL files in ${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/ (excluding README.md)
  3. Read ${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/references/prompt-template.md
  4. Apply the confidentiality rules from the prompt template (no financials, no client names, no pipeline, no team member names)
  5. Generate 2-4 post options based on the source material, written in the user's personal style
  6. Present them in a clean, copy-paste-ready format

Flags

Flag Behavior
(none) Normal generation flow
--setup Re-run style setup, overwrite existing config
--setup-sources Configure which Fireflies, Slack, and Drive sources to pull from
--refresh Pull fresh source material from configured integrations, then generate
--view-style Read and display ~/.config/casper/linkedin-style.md
--view-sources List and summarize all files in source-material/
--add-source Prompt user to paste new content, save as new .md file in source-material/

Flag Details

--setup-sources

Interactive setup for automatic source pulling. Read references/source-integrations.md for full details.

  1. Ask: "What's your work email address? This is used to filter transcripts to only meetings you attended."
    • Save as user_email in the config
  2. Ask: "Which sources do you want to connect?" Present options:
    • Fireflies.ai — pulls meeting transcripts (needs FIREFLIES_API_KEY env var)
    • Slack — pulls messages from channels (needs SLACK_BOT_TOKEN env var)
    • Google Drive — pulls docs and transcripts (needs OAuth setup via google-workspace skill)
  3. For each selected source, check if the required env var / credentials exist. If missing, provide setup instructions:
    • Fireflies: "Set FIREFLIES_API_KEY in your environment. Get your API key from https://app.fireflies.ai/api"
    • Slack: "Set SLACK_BOT_TOKEN in your environment. Create a Slack app at https://api.slack.com/apps"
    • Google Drive: "Run the google-workspace skill setup to configure OAuth."
  4. For each enabled source, gather configuration:
    • Fireflies: search terms (or leave empty for all recent) and days_back
    • Slack: which channels to pull from — list available channels if possible, otherwise ask the user
    • Google Drive: search terms, days_back
  5. Save to ~/.config/casper/linkedin-sources.md
  6. Confirm: "Source config saved. Run /casper:generate-linkedin-post --refresh to pull fresh content."

--refresh

Pull fresh source material from all configured integrations before generating posts. Read references/source-integrations.md for the full integration workflow.

Summary of the flow:

  1. Read ~/.config/casper/linkedin-sources.md — if missing, run --setup-sources first
  2. For each enabled source, call the existing Casper skill scripts:
    • Fireflies: python ${CLAUDE_PLUGIN_ROOT}/skills/transcript-search/scripts/fireflies_transcript_search.py "{term}" --days-back {N} --content --json
      • After fetching, filter results to only transcripts where user_email (from source config) appears in the transcript's participants array
    • Slack: python ${CLAUDE_PLUGIN_ROOT}/skills/slack-automation/scripts/slack_search.py read "{channel}" --days {N}
    • Google Drive: python ${CLAUDE_PLUGIN_ROOT}/skills/google-workspace/scripts/gdrive_search.py files "{term}" --modified-days {N} --json
  3. Convert JSON output to clean markdown and save to source-material/:
    • Fireflies: fireflies-{YYYY-MM-DD}-{title-slug}.md
    • Slack: slack-{channel}-{YYYY-MM-DD}.md
    • Google Drive: gdrive-{title-slug}-{YYYY-MM-DD}.md
  4. Proceed with normal generation

--view-style

Read ~/.config/casper/linkedin-style.md and display it. If it doesn't exist, say "No style profile found. Run /casper:generate-linkedin-post --setup to create one."

--view-sources

List all .md files in ${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/ (excluding README.md). For each file, show the filename and a 1-line summary of its contents.

--add-source

  1. Ask: "Paste the content you want to add as source material (meeting transcript, Slack dump, notes, etc.)"
  2. Ask: "What should I name this source file? (e.g., team-standup-jan-2025)"
  3. Save as ${CLAUDE_PLUGIN_ROOT}/skills/linkedin-post-generator/source-material/[name].md
  4. Confirm: "Source material saved. It will be included in future post generation."

Reference Files

File When to Read
references/prompt-template.md Every generation run — contains voice rules, few-shot examples, confidentiality rules
references/source-integrations.md When running --refresh or --setup-sources — contains script paths, arguments, output conversion
references/style-setup.md When running --setup — contains analysis framework for style profiling
source-material/*.md Every generation run — raw content to extract post ideas from
how to use linkedin-post-generator

How to use linkedin-post-generator on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add linkedin-post-generator
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/casper-studios/casper-marketplace --skill linkedin-post-generator

The skills CLI fetches linkedin-post-generator from GitHub repository casper-studios/casper-marketplace and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/linkedin-post-generator

Reload or restart Cursor to activate linkedin-post-generator. Access the skill through slash commands (e.g., /linkedin-post-generator) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.630 reviews
  • Zaid Desai· Dec 20, 2024

    linkedin-post-generator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chaitanya Patil· Dec 12, 2024

    We added linkedin-post-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Naina Kapoor· Dec 4, 2024

    I recommend linkedin-post-generator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Naina Sharma· Nov 27, 2024

    Useful defaults in linkedin-post-generator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Meera Sethi· Nov 23, 2024

    Keeps context tight: linkedin-post-generator is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Advait Thomas· Nov 11, 2024

    We added linkedin-post-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Piyush G· Nov 3, 2024

    linkedin-post-generator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Shikha Mishra· Oct 22, 2024

    linkedin-post-generator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Tariq Haddad· Oct 18, 2024

    linkedin-post-generator has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Anaya Liu· Oct 14, 2024

    Registry listing for linkedin-post-generator matched our evaluation — installs cleanly and behaves as described in the markdown.

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